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Early Anthropic hire, former METR COO have found a way to rein in rogue AI agents

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Why This Matters

Enterprise AI adoption is increasingly stalled by liability and trust concerns rather than model capability, and AIUC is betting that a SOC 2-style audit and certification layer can unblock deployments in regulated sectors. With $55M raised and customers like Cursor, Harvey, and ElevenLabs, it signals the emergence of an AI assurance industry built by ex-Anthropic and ex-METR insiders.

Key Takeaways
Worth a Look

The Alignment Problem by Brian Christian — If AIUC's mission to keep AI agents from going rogue inside enterprises grabbed you, Brian Christian's The Alignment Problem is the deep dive into why steering machine learning systems toward human values is so hard. It's an accessible, well-reported tour of the researchers wrestling with exactly the risks that pushed founders like Kvist and Dattani to build a safety startup.

See The Alignment Problem by Brian Christian on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.

A day after Anthropic researcher Jacob Coxon quit his job over concerns that AI could kill us all by the end of the decade, I met with founders and brothers-in-law Rune Kvist and Rajiv Dattani. They think they have a solution that could save us all, or at least help prevent AI agents from going rogue inside enterprises.

“AI is getting smarter at an increasingly rapid rate. The surprising thing about AI is that it becomes harder to adopt and harder to control as AI gets smarter, not easier,” said Kvist, an early Anthropic employee who is also married to Dattani’s sister). Dattani is the former COO of the AI safety research organization METR.

The pair launched a startup called Artificial Intelligence Underwriting Company (AIUC) that hopes to bring AI safety to enterprises and companies building AI models and agents. The startup names Cursor, Lovable, Harvey, and ElevenLabs as customers.

On Tuesday, AIUC announced a $40 million Series A led by Ribbit Capital, with participation from First Harmonic. It previously closed a $15 million seed round from Nat Friedman through his fund NFDG, along with Emergence, Terrain, and Anthropic co-founder Ben Mann, among others, bringing its total funding to $55 million.

What caught the attention of this A-list group of investors is AIUC’s attempt to apply a familiar cybersecurity model to a new set of AI risks. The company has built a third-party audit and certification layer for AI agents.

“Banks, hospitals, governments and militaries no longer decline to deploy AI because a model isn’t smart enough,” Kvist said. “They decline because they’ve made commitments to their own customers about what a system will and won’t do, and nobody can currently guarantee that.”

Using the widely adopted cybersecurity standard SOC 2 as its muse, AIUC has developed a standard called AIUC-1 and a testing service to validate agents against the standard.

To build the standard, AIUC assembled a consortium of about 250 security and risk leaders — the buyers of agents. “These are the people who we meet with on a monthly basis, and the question we ask them is: When you’re buying agents from someone, what would you look for? What are the questions you’d want to ask, and what would you want to see addressed?” Dattani told TechCrunch.

That feedback shapes the tests. The startup then runs an agent through a suite of some 5,000 tests to see how it behaves in scenarios involving jailbreaks, hallucinations, and data leaks. The results produce a roughly 100-page report detailing where an agent performs safely and reliably — and where it doesn’t. Interestingly, AIUC uses AI agents to run the tests and AI to analyze the data. Humans, however, verify the final audit, Kvist said.

If this sounds a bit familiar, it is. Dattani’s former employer METR, where he was COO from 2024 to 2025 and remains a board member, does similar testing for the frontier labs, though its work until recently has focused mostly on performance (whether agents can reliably complete tasks). METR was one of the independent research orgs OpenAI used to investigate its Hugging Face incident.

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